Learning NEAT Emergent Behaviors in Robot Swarms

TL;DR

This paper introduces an evolutionary algorithm to train individual robot behaviors that lead to desired emergent group behaviors in swarm robotics, validated through simulations of aerial and ground robots performing complex tasks.

Contribution

It presents a novel method for learning emergent behaviors in robot swarms using evolution, applicable across different robot platforms and tasks.

Findings

Successful emergence of complex behaviors like area coverage and wall climbing

Algorithm outperforms designed policies in simulated tasks

Effective across aerial and ground robot simulations

Abstract

When researching robot swarms, many studies observe complex group behavior emerging from the individual agents' simple local actions. However, the task of learning an individual policy to produce a desired group behavior remains a challenging problem. We present a method of training distributed robotic swarm algorithms to produce emergent behavior. Inspired by the biological evolution of emergent behavior in animals, we use an evolutionary algorithm to train a population of individual behaviors to produce a desired group behavior. We perform experiments using simulations of the Georgia Tech Miniature Autonomous Blimps (GT-MABs) aerial robotics platforms conducted in the CoppeliaSim simulator. Additionally, we test on simulations of Anki Vector robots to display our algorithm's effectiveness on various modes of actuation. We evaluate our algorithm on various tasks where a somewhat complex group behavior is required for success. These tasks include an Area Coverage task and a Wall Climb task. We compare behaviors evolved using our algorithm against designed policies, which we create in order to exhibit the emergent behaviors we desire.